Tracking Antibodies

An ongoing experiment to track antibodies over time

Introduction

Objective: To Monitor changes in 1) neutralising antibody levels 2) rate of waning 3) days to seronegative status.

How do Antibodies Work?

vembedr::embed_youtube(id = "qCRwuxDpthY",
                       allowfullscreen = T) %>%
  use_align("center")

Neutralizing Antibodies Against Coronaviruses

knitr::include_graphics("img/PBjorkman-CBarnes.jpg")

Credit - C. Barnes / Björkman laboratory

Dr. Pamela Bjorkman

vembedr::embed_youtube(id = "Sp1aVTj7IvI",
                       allowfullscreen = T) %>%
  use_align("center")

Results

sero_raw <- readxl::read_excel("~/Downloads/Santanu_COVID_events_research.xlsx", 
                               sheet = "long") %>% 
  clean_names() %>% 
  mutate(date = ymd(date))

sero <- sero_raw %>% 
  rename(start = date,
         content = event) %>% 
  mutate(sero = parse_number(outcome)) %>% 
  mutate(content = str_remove(content, "SARS-CoV-2 ANTI-SPIKE IgG ANTIBODIES")) %>% 
  mutate(content = str_remove(content, "\\:")) %>% 
  mutate(content = str_remove(content, "\\*"))

Timeline

sero %>% 
  timevis()

IgG Antibody levels over time

fig_igg <- sero %>% 
  filter(!is.na(sero)) %>% 
  ggplot(aes(start, sero)) +
  geom_line()  +
  geom_point(aes(size = sero)) +
  scale_x_date(labels = scales::date_format("%Y %b")) +
  labs(title = "Trend in IgG antibody levels",
       x = "",
       y = "IgG Antibodies (in AU/ml)",
       size = "") +
  theme(legend.position = "top")

ggplotly(fig_igg)

Source Code

---
title: "Tracking Antibodies"
author: 
- name: "SP"
- name: "AM"
description: "An ongoing experiment to track antibodies over time"
date: 2022-04-08
execute:
  warning: false
  message: false
format:
  html:
    html-math-method: katex
    code-tools: true
    self-contained: true
categories: ["COVID-19", "Antibodies", "Public Health"]
image: PBjorkman-CBarnes-SARS-CoV2-Antibodies-WEB.original.jpg
---

```{r}
#| echo: false
#| message: false
#| warning: false

# knitr::opts_chunk$set(echo = F, message = F, warning = F, fig.height = 6, fig.width = 9, fig.align = "centre")

library(tidyverse)
library(janitor)
library(lubridate)

library(timevis)
library(plotly)
library(vembedr)

theme_set(theme_minimal())

```

# Introduction

| **Objective**: To Monitor changes in 1) neutralising antibody levels 2) rate of waning 3) days to seronegative status.

## How do Antibodies Work?

```{r}
vembedr::embed_youtube(id = "qCRwuxDpthY",
                       allowfullscreen = T) %>%
  use_align("center")

```

## Neutralizing Antibodies Against Coronaviruses

```{r}
#| fig-cap: A SARS-CoV-2 S trimer bound by a neutralizing antibody.
#| fig-subcap: Credit - C. Barnes / Björkman laboratory
knitr::include_graphics("img/PBjorkman-CBarnes.jpg")
```

## Dr. Pamela Bjorkman

```{r}
vembedr::embed_youtube(id = "Sp1aVTj7IvI",
                       allowfullscreen = T) %>%
  use_align("center")

```

# Results

```{r}
sero_raw <- readxl::read_excel("~/Downloads/Santanu_COVID_events_research.xlsx", 
                               sheet = "long") %>% 
  clean_names() %>% 
  mutate(date = ymd(date))

sero <- sero_raw %>% 
  rename(start = date,
         content = event) %>% 
  mutate(sero = parse_number(outcome)) %>% 
  mutate(content = str_remove(content, "SARS-CoV-2 ANTI-SPIKE IgG ANTIBODIES")) %>% 
  mutate(content = str_remove(content, "\\:")) %>% 
  mutate(content = str_remove(content, "\\*"))

```

## Timeline

```{r}
#| column: screen

sero %>% 
  timevis()

```

## IgG Antibody levels over time

```{r}
fig_igg <- sero %>% 
  filter(!is.na(sero)) %>% 
  ggplot(aes(start, sero)) +
  geom_line()  +
  geom_point(aes(size = sero)) +
  scale_x_date(labels = scales::date_format("%Y %b")) +
  labs(title = "Trend in IgG antibody levels",
       x = "",
       y = "IgG Antibodies (in AU/ml)",
       size = "") +
  theme(legend.position = "top")

ggplotly(fig_igg)

```